How to Build Your First AI Agent Using Model Context Protocol and Python
Building an AI agent involves more than connecting a language model to a chatbot interface. A useful agent needs access to external tools, the ability to process information, and a controlled way to perform tasks on behalf of a user. For example, an AI agent could calculate figures, retrieve information from a local database, read documents, create reports, or interact with external services. However, giving an AI model access to these capabilities introduces an important technical challenge: how do you connect the model to external tools without building a separate integration for every application? The Model Context Protocol (MCP) provides a standard approach to this problem. MCP allows AI applications to communicate with external tools and data sources through a common protocol. Instead of building a different connection for every model and application, developers can create MCP servers that expose specific capabilities to compatible AI clients. Python makes this process accessible ...